复杂环境下角色动画的自动化选择与合成的研究实现
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摘要
随着计算机硬件与计算机图形学算法的不断发展,计算机动画正在成为当前研究的一大热点。在该领域,角色动画是一个重要的研究方向。它在数字娱乐领域取得了巨大的成功,但还存在着很多问题尚待解决。
角色动画是对真实世界中人物的模拟,要想让角色动画逼真,让人具有代入感,保证角色的真实感是唯一的途径。然而,角色具有多个自由度和非常复杂的肢体运动,且外形不规则,运动的风格以及脸部表情千变万化。人类对自身的运动又非常熟悉,经常能够从远处仅通过走路的姿态就识别出熟悉的人,且容易察觉其不协调的运动。因此,如何保证运动的真实感是一个重要的问题。这也是本文研究的第一个问题。
通过运动捕获数据驱动的角色动画,由于其数据来源于真实世界中表演者的活动,所以能够保证运动的真实感。因此,本论文采用基于运动数据的方法来制作角色动画。然而该方法原理上是三维运动的简单录制和重放,无法提供对运动数据更多的交互控制。这使得该方法的数据可重用性低,难以适应交互性强的应用。
为了解决该问题,本论文采用运动图的方法将运动捕获数据构造成运动图,并将生成目标运动的问题转换为在该图中搜索一条路径的等价问题。将运动捕获数据中的每一帧的姿势作为运动图的一个节点,再找到这些姿势中相近的姿势,为其创建运动之间的过渡,作为运动图的边。就将原始固定为线性结构的运动捕获数据重构为灵活性更好的图状结构的运动图。然而,运动图方法也有其固有的缺陷,那就是生成运动的效率与功能性受制于构成它的运动捕获数据库,令人诟病。
为了弥补运动图的缺陷,本文采用了扩展的方法对运动图进行扩展,并采用了相应的压缩算法对运动图进行了压缩。扩展的目的是为了增加运动图的功能性,而压缩的目的是为了降低运动图的容量,提高运动图生成运动的效率。
当前的角色动画制作技术,依赖于有经验的动画师,是一项需要大量的体力与脑力劳动的工作。提高角色动画制作效率,减少动画师的参与,能够有效降低角色动画制作的时间与制作成本。然而,由于角色动画制作自身的复杂性与不确定性,如何提高角色动画制作效率也是一个具有挑战性的问题。这是本文研究的第二个问题。
为了解决角色动画制作效率不高的问题,本文针对角色动画常见的应用是生成满足指定环境的动画,提出了基于运动图在复杂环境中自动合成角色动画的一种方法,对提高角色动画制作效率进行了一个有意义的探索。
Along with the continuous development of the computer hardware and the computer graphics algorithm, computer animation is becoming the hottest focus of current research. In the field of computer animation, character animation is a popular research direction. It has achieved great success in the field of digital entertainment, but there are still a lot of problems need to be solved.
Character animation is the animating of characters in really environment, ensuring the reality of characters is the only way to make human motion real and increase the immersion. However, a character has multiple degrees of freedom, and its limb movements are complex. Besides, the figure is irregular, and movement style is protean. Human are familiar with their own movements, and they can easily detect the uncoordinated movement, even a walking posture can give enough information to identify an acquaintance. So, keeping the reality of the human motion is an important problem , also the first problem we focus on in this thesis.
Research shows that character animation methods based on motion data can ensure the reality of character, because the data come from the real world activities of the performers. So, the motion data method is used to drive the character. However, the principle of the method is a simple recording and playback of three dimensional movement, it cannot provide more interactive controlling. This makes the data cannot be reuse and difficult to adapt to the application of strong interactions.
In order to solve the problem, the motion data is built into a motion graph, so target motion generating is changed into graph search problem. This method makes every frame of motion data a node of motion graph, and create transitions between similar postures as edges of the graph. After that, linear structure of the original motion data is reconstructed as more flexibility graphic structure. However, the motion graph method has its inherent fault which the efficiency and functional of generated movements subject to its original motion capture database.
In order to solve the defects of motion graph method, the motion graph is expanded and compressed. The purpose of expansion is increasing the function, and that of compression is reducing the capacity of the graph.
The current character animation techniques rely on experienced animators, and is a heavy physical and mental work. Increasing the efficiency of character animation and reducing the participation of animators can effectively reduce the time and character animation production costs. However, due to its complexity and uncertainty, improving the animation production efficiency is a challenge. This is the second problem which is solved in this thesis.
The usual application of character animation is producing animation of certain environment. In order to improve efficiency of character animation, a method to automatic synthesis character animation in complex environment is proposed.
角色动画是对真实世界中人物的模拟,要想让角色动画逼真,让人具有代入感,保证角色的真实感是唯一的途径。然而,角色具有多个自由度和非常复杂的肢体运动,且外形不规则,运动的风格以及脸部表情千变万化。人类对自身的运动又非常熟悉,经常能够从远处仅通过走路的姿态就识别出熟悉的人,且容易察觉其不协调的运动。因此,如何保证运动的真实感是一个重要的问题。这也是本文研究的第一个问题。
通过运动捕获数据驱动的角色动画,由于其数据来源于真实世界中表演者的活动,所以能够保证运动的真实感。因此,本论文采用基于运动数据的方法来制作角色动画。然而该方法原理上是三维运动的简单录制和重放,无法提供对运动数据更多的交互控制。这使得该方法的数据可重用性低,难以适应交互性强的应用。
为了解决该问题,本论文采用运动图的方法将运动捕获数据构造成运动图,并将生成目标运动的问题转换为在该图中搜索一条路径的等价问题。将运动捕获数据中的每一帧的姿势作为运动图的一个节点,再找到这些姿势中相近的姿势,为其创建运动之间的过渡,作为运动图的边。就将原始固定为线性结构的运动捕获数据重构为灵活性更好的图状结构的运动图。然而,运动图方法也有其固有的缺陷,那就是生成运动的效率与功能性受制于构成它的运动捕获数据库,令人诟病。
为了弥补运动图的缺陷,本文采用了扩展的方法对运动图进行扩展,并采用了相应的压缩算法对运动图进行了压缩。扩展的目的是为了增加运动图的功能性,而压缩的目的是为了降低运动图的容量,提高运动图生成运动的效率。
当前的角色动画制作技术,依赖于有经验的动画师,是一项需要大量的体力与脑力劳动的工作。提高角色动画制作效率,减少动画师的参与,能够有效降低角色动画制作的时间与制作成本。然而,由于角色动画制作自身的复杂性与不确定性,如何提高角色动画制作效率也是一个具有挑战性的问题。这是本文研究的第二个问题。
为了解决角色动画制作效率不高的问题,本文针对角色动画常见的应用是生成满足指定环境的动画,提出了基于运动图在复杂环境中自动合成角色动画的一种方法,对提高角色动画制作效率进行了一个有意义的探索。
Along with the continuous development of the computer hardware and the computer graphics algorithm, computer animation is becoming the hottest focus of current research. In the field of computer animation, character animation is a popular research direction. It has achieved great success in the field of digital entertainment, but there are still a lot of problems need to be solved.
Character animation is the animating of characters in really environment, ensuring the reality of characters is the only way to make human motion real and increase the immersion. However, a character has multiple degrees of freedom, and its limb movements are complex. Besides, the figure is irregular, and movement style is protean. Human are familiar with their own movements, and they can easily detect the uncoordinated movement, even a walking posture can give enough information to identify an acquaintance. So, keeping the reality of the human motion is an important problem , also the first problem we focus on in this thesis.
Research shows that character animation methods based on motion data can ensure the reality of character, because the data come from the real world activities of the performers. So, the motion data method is used to drive the character. However, the principle of the method is a simple recording and playback of three dimensional movement, it cannot provide more interactive controlling. This makes the data cannot be reuse and difficult to adapt to the application of strong interactions.
In order to solve the problem, the motion data is built into a motion graph, so target motion generating is changed into graph search problem. This method makes every frame of motion data a node of motion graph, and create transitions between similar postures as edges of the graph. After that, linear structure of the original motion data is reconstructed as more flexibility graphic structure. However, the motion graph method has its inherent fault which the efficiency and functional of generated movements subject to its original motion capture database.
In order to solve the defects of motion graph method, the motion graph is expanded and compressed. The purpose of expansion is increasing the function, and that of compression is reducing the capacity of the graph.
The current character animation techniques rely on experienced animators, and is a heavy physical and mental work. Increasing the efficiency of character animation and reducing the participation of animators can effectively reduce the time and character animation production costs. However, due to its complexity and uncertainty, improving the animation production efficiency is a challenge. This is the second problem which is solved in this thesis.
The usual application of character animation is producing animation of certain environment. In order to improve efficiency of character animation, a method to automatic synthesis character animation in complex environment is proposed.
引文
[1] Issac kerlow. The Art of 3D Computer Animation and Effects. Publisher: Wiley,2009
[2] Frederic I. Parke, Keith Waters. computer facial animation 2rd edition. Publisher: AK Peters,1998
[3] Alan Watt,Fabio Policarpo. 3D游戏卷2动画与高级实时渲染技术。机械工业出版社,2005
[4]环球数码媒体科技研究有限公司。魔比斯环一部CG电影的诞生。电子工业出版社,2005
[5] Booth, Kochanek. Computer animation. IEEE Spectrum, 1983, Vol.20(2): 44-51
[6] H Togawa, M Okuda .Position-based keyframe selection for human motion animation. International Conference on Parallel and Distributed Systems,2005, Vol.2:182-185
[7] M Yamamoto, Y Ohta, T Yamagiwa, K Yagishita. Human action tracking guided by keyframes. Fourth IEEE International Conference on Automatic Face and Gesture Recognition, 2000,354–361
[8] Scott N. Steketee , Norman I. Badler. Parametric keyframe interpolation incorporating kinetic adjustment and phrasing control. Proceedings of the 12th annual conference on Computer graphics and interactive techniques, 1985,255-262
[9] Mohd Izani , Ahmad Rafi Eshaq , Aishah Razak , Norzaiha Norhan. A Study of Practical Approach of Using Motion Capture and Keyframe Animation Techniques. Proceedings of the International Conference on Computer Graphics, Imaging and Visualization, 2004, 52-54
[10] Franck Multon, Laure France, etc. Compute Animation of Human Walking: a Survey. ACM SIGGRAPH 1999
[11] Ik Soo Lim, Daniel Thalmann. Key-posture extraction out of human motion data, Proceeding of 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2001,Vol.2:1167-1169
[12] S. Li, M. Okuda, S. Takahashi. Embedded Key Frame Extraction for CG Animation by Frame Decimation. ICME, 2005
[13] D. G. Lowe. Three-dimensional object recognition from single two dimensional images Artificial Intelligence, 1987,Vol.31:355-395
[14] Liu, F.Zhuang, etc. 3d motion retrieval with motion index tree. Comput. Vis. Image Understand. 2003,Vol.92(2):265–284
[15] Gong, Y, Liu, X. Video summarization using singular value decomposition. Computer Vision and Pattern Recognition, 2000,174-180
[16] Cooper, etc. Summarizing video using nonnegative similarity matrix factorization. IEEE Workshop on Multimedia Signal Processing,2002, 25– 28
[17] Steketee S. Parametric keyframe interpolation incorating kinetic adjustment and phrasing control. Computer Graphics,1985
[18] H ElMaraghy. Kinematic and geometric modeling and animation of robots. Graphics Interface, 1986
[19] G Migadis, KJ Kyriakopoulos. Design and forward kinematic analysis of a robotic snake. IEEE International Conference on Robotics and Automation, 1997
[20] Serra L, Chua TS. A frame-based 3D graphics system for kinematic animation with constraints. Proc Comput Graph International , 1990,299–316
[21] D Li, H Xu, R Huang, J Wang. Kinematic Inbetweening for Motion Animation. Lecture Notes in Computer Science, 2007
[22] J Wilhelms, Virya. A motion control editor for kinematic and dynamic animation. Proceedings on Graphics Interface, 1986,141-146
[23] Thomann, Derik. Design of human-like posture prediction for inverse kinematic posture control of a humanoid robot. Massachusetts Institute of Technology,2005
[24] J Cheong, WK Chung, Y Youm . Inverse kinematics of multilink flexible robots for high-speed applications. IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION,2004
[25] Nakamura Y, Hanafusa H. Inverse kinematic solutions with singularity robustness for robot manipulator control. Journal of Dynamic Systems,measurement, and Control, 1986,Vol.108:163-171
[26] Doris H. U. Kochanek, Richard H. Bartels. Interpolating splines with local tension, continuity, and bias control. ACM SIGGRAPH 1984
[27] LCT Wang, CC Chen,. A combined optimization method for solving the inverse kinematics problem of mechanical manipulators. IEEE Transactions on Robotics and Automation, 1991, Vol.7:489-499.
[28] D. E. Whitney. Resolved motion rate control of manipulators and human prostheses. IEEE Transactions on Man-Machine Systems, 1969, Vol.10:47-53.
[29] W. A. Wolovich, H. Elliot, A computational technique for inverse kinematics. IEEE Conference on Decision and Control, 1984, 1359-1363.
[30] A. Balestrino, G. De Maria, and L. Sciavicco. Robust control of robotic manipulators. IFAC World Congress, 1984, Vol. 5: 2435-2440.
[31] Y. Nakamura and H. Hanafusa. Inverse kinematics solutions with singularity robustness for robot manipulator control. Journal of Dynamic Systems, Measurement, and Control, 1986, Vol.108 :163-171.
[32] C. W. Wampler. Manipulator inverse kinematic solutions based on vector formulations and damped least squares methods. IEEE Transactions on Systems, Man, and Cybernetics, 1986, Vol.16:93-101
[33] J. Zhao and N. I. Badler. Inverse kinematics positioning using nonlinear programming for highly articulated figures. ACM Transactions on Graphics,1994,Vol.13:313-336
[34] E. Oyama, N. Y. Chong, A. Agah, T. Maeda, and S. Tachi. Inverse kinematics learning by modular architecture neural networks with performance prediction networks. IEEE International Conference on Robotics and Automation, 2001, 1006-1012.
[35] R. Grzeszczuk, D. Terzopoulos, and G. Hinton, NeuroAnimator: Fast neural network emulation and control of physics-based models, in Proc. ACM SIGGRAPH,1998 ,pp. 9-20.
[36] P. Baerlocher and R. Boilic. Inverse Kinematics Techniques for the Interactive Posture Control of Articulated Figures. PhD thesis, Ecole Poly technique Federale de Lausanne, 2001.
[37] N. I. Badler, K. H. Manoochehri, and G. Walters. Articulated figure positioning by multiple constraints. IEEE Computer Graphics and Applications, 1987, Vol.7:28-38
[38] KJ Choi, etc. On-line motion retargeting. Journal of Visualization and Computer Animation, 2000, Vol.11:223-235
[39] M. Girard. Interactive design of 3D computer-animated legged animal motion. IEEE Computer Graphics and Applications, 1987,Vol.7:39-51
[40] G. T. Ke. Solving inverse kinematics constraint problems for highly articulated models. Master's thesis, University of Waterloo, 2000
[41] Richard S. Hartenberg and Jacques Denavit . Kinematic synthesis of linkages, 1964
[42] A Bruderlin, TW Calvert .Goal-directed, dynamic animation of human walking. Computer graphics and interactive techniques, 1989
[43] J Wilhelms, M Moore, R Skinner .Dynamic animation: interaction and control. The Visual Computer, 1988
[44] J Ambrósio, M Silva . Kinematic Data Consistency in the Inverse Dynamic Analysis of Biomechanical Systems. Multibody System Dynamics, 2004,219-239
[45] N Bhushan, R Shadmehr .Evidence for learning of a forward dynamic model in human adaptive control. Journal of Cognitive Neuroscience, 1998
[46] Joshua D. Webb,etc.A three dimensional forward dynamic model of a human knee for determining ligament forces. ASME International Mechanical Engineering Congress: Advances in Bioengineering, 2003
[47] HH West, ML McGuire .Dynamic programming approach to optimal feedback-feed-forward controller design. Journal of Engineering Mathematics, 1970
[48] WQD Do, DCH Yang , Inverse dynamic analysis and simulation of a platform type of robot.Journal of Robotic Systems, 1988
[49] R Happee . Inverse dynamic optimization including muscular dynamics, a new simulation method applied to goal directed movements. Journal of biomechanics, 1994
[50] Jehee Lee, etc. Interactive control of avatars animated with human motion data. Proceedings of the 29th annual conference on Computer graphics and interactive techniques, 2002,23-26
[51] B Cramblitt. Computers capture moments of motion. COMP GRAPH. WORLD,1989
[52] K Pullen, C Bregler. Motion capture assisted animation: Texturing and synthesis. International Conference on Computer Graphics and Interactive Techniques,2002
[53] VB Zordan, JK Hodgins .Motion capture-driven simulations that hit and react. ACM SIGGRAPH, 2002
[54] TB Moeslund, E Granum. A survey of computer vision-based human motion capture. Computer Vision and Image Understanding, 2001
[55] T Polichroniadis, N Dodgson. Motion blending using a classifier system.
[56] International Conference in Central Europe on Computer Graphics, Visualization and Interactive Digital Media, 1999
[57] A Witkin, Z Popovic. Motion warping. International Conference on Computer Graphics and Interactive Technique, 1999
[58] F Pighin, JP Lewis. Facial motion retargeting. ACM SIGGRAPH, 2006
[59] Seguier, Renaud, Breton, Gaspard, Stoiber, Nicolas. Facial animation retargeting and control based on a human appearance space. Computer Animation and Virtual Worlds, 2010.1, Vol.21:39-54
[60] Y Li, T Wang, HY Shum. Motion texture: a two-level statistical model for character motion synthesis. International Conference on Computer Graphics and Interactive Techniques,2002
[61] K Pullen, C Bregler .Motion capture assisted animation: Texturing and synthesis. International Conference on Computer Graphics and Interactive Techniques,2002
[62] S Moradoff, D Lischinski .Constrained synthesis of textural motion for articulated characters. The Visual Computer, 2004r
[63] M.Brand and A.Hertzmann. Style machine.ACM SIGGRAPH 2000
[64] R.Urtasun,E Glardon,R.Boulic,D.Thalmann,E Fua. Style-Based Motion Synthesis. Computer Graphics Forum,2004, vol.23:799-812
[65] Sidenbladh,H.,B1ack,M.J.,And Sigal,L.Implict probabilistic models of human motion for synthesis and tracking. European Conference on Computer Vision,2002
[66] KOVAR, L., GLEICHER, M., AND PIGHIN, F. Motion graphs. ACM SIGGRAPH 2002
[67] LEE, J, etc. Interactive control of avatars animated with human motion data. ACM SIGGRAPH 2002
[68] ARIKAN, O, etc. Interactive motion generation from examples. ACM SIGGRAPH 2002
[69] Alla Safonova, Jessica K. Hodgins .Construction and optimal search of interpolated motion graph.ACM SIGGRAPH 2007
[2] Frederic I. Parke, Keith Waters. computer facial animation 2rd edition. Publisher: AK Peters,1998
[3] Alan Watt,Fabio Policarpo. 3D游戏卷2动画与高级实时渲染技术。机械工业出版社,2005
[4]环球数码媒体科技研究有限公司。魔比斯环一部CG电影的诞生。电子工业出版社,2005
[5] Booth, Kochanek. Computer animation. IEEE Spectrum, 1983, Vol.20(2): 44-51
[6] H Togawa, M Okuda .Position-based keyframe selection for human motion animation. International Conference on Parallel and Distributed Systems,2005, Vol.2:182-185
[7] M Yamamoto, Y Ohta, T Yamagiwa, K Yagishita. Human action tracking guided by keyframes. Fourth IEEE International Conference on Automatic Face and Gesture Recognition, 2000,354–361
[8] Scott N. Steketee , Norman I. Badler. Parametric keyframe interpolation incorporating kinetic adjustment and phrasing control. Proceedings of the 12th annual conference on Computer graphics and interactive techniques, 1985,255-262
[9] Mohd Izani , Ahmad Rafi Eshaq , Aishah Razak , Norzaiha Norhan. A Study of Practical Approach of Using Motion Capture and Keyframe Animation Techniques. Proceedings of the International Conference on Computer Graphics, Imaging and Visualization, 2004, 52-54
[10] Franck Multon, Laure France, etc. Compute Animation of Human Walking: a Survey. ACM SIGGRAPH 1999
[11] Ik Soo Lim, Daniel Thalmann. Key-posture extraction out of human motion data, Proceeding of 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2001,Vol.2:1167-1169
[12] S. Li, M. Okuda, S. Takahashi. Embedded Key Frame Extraction for CG Animation by Frame Decimation. ICME, 2005
[13] D. G. Lowe. Three-dimensional object recognition from single two dimensional images Artificial Intelligence, 1987,Vol.31:355-395
[14] Liu, F.Zhuang, etc. 3d motion retrieval with motion index tree. Comput. Vis. Image Understand. 2003,Vol.92(2):265–284
[15] Gong, Y, Liu, X. Video summarization using singular value decomposition. Computer Vision and Pattern Recognition, 2000,174-180
[16] Cooper, etc. Summarizing video using nonnegative similarity matrix factorization. IEEE Workshop on Multimedia Signal Processing,2002, 25– 28
[17] Steketee S. Parametric keyframe interpolation incorating kinetic adjustment and phrasing control. Computer Graphics,1985
[18] H ElMaraghy. Kinematic and geometric modeling and animation of robots. Graphics Interface, 1986
[19] G Migadis, KJ Kyriakopoulos. Design and forward kinematic analysis of a robotic snake. IEEE International Conference on Robotics and Automation, 1997
[20] Serra L, Chua TS. A frame-based 3D graphics system for kinematic animation with constraints. Proc Comput Graph International , 1990,299–316
[21] D Li, H Xu, R Huang, J Wang. Kinematic Inbetweening for Motion Animation. Lecture Notes in Computer Science, 2007
[22] J Wilhelms, Virya. A motion control editor for kinematic and dynamic animation. Proceedings on Graphics Interface, 1986,141-146
[23] Thomann, Derik. Design of human-like posture prediction for inverse kinematic posture control of a humanoid robot. Massachusetts Institute of Technology,2005
[24] J Cheong, WK Chung, Y Youm . Inverse kinematics of multilink flexible robots for high-speed applications. IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION,2004
[25] Nakamura Y, Hanafusa H. Inverse kinematic solutions with singularity robustness for robot manipulator control. Journal of Dynamic Systems,measurement, and Control, 1986,Vol.108:163-171
[26] Doris H. U. Kochanek, Richard H. Bartels. Interpolating splines with local tension, continuity, and bias control. ACM SIGGRAPH 1984
[27] LCT Wang, CC Chen,. A combined optimization method for solving the inverse kinematics problem of mechanical manipulators. IEEE Transactions on Robotics and Automation, 1991, Vol.7:489-499.
[28] D. E. Whitney. Resolved motion rate control of manipulators and human prostheses. IEEE Transactions on Man-Machine Systems, 1969, Vol.10:47-53.
[29] W. A. Wolovich, H. Elliot, A computational technique for inverse kinematics. IEEE Conference on Decision and Control, 1984, 1359-1363.
[30] A. Balestrino, G. De Maria, and L. Sciavicco. Robust control of robotic manipulators. IFAC World Congress, 1984, Vol. 5: 2435-2440.
[31] Y. Nakamura and H. Hanafusa. Inverse kinematics solutions with singularity robustness for robot manipulator control. Journal of Dynamic Systems, Measurement, and Control, 1986, Vol.108 :163-171.
[32] C. W. Wampler. Manipulator inverse kinematic solutions based on vector formulations and damped least squares methods. IEEE Transactions on Systems, Man, and Cybernetics, 1986, Vol.16:93-101
[33] J. Zhao and N. I. Badler. Inverse kinematics positioning using nonlinear programming for highly articulated figures. ACM Transactions on Graphics,1994,Vol.13:313-336
[34] E. Oyama, N. Y. Chong, A. Agah, T. Maeda, and S. Tachi. Inverse kinematics learning by modular architecture neural networks with performance prediction networks. IEEE International Conference on Robotics and Automation, 2001, 1006-1012.
[35] R. Grzeszczuk, D. Terzopoulos, and G. Hinton, NeuroAnimator: Fast neural network emulation and control of physics-based models, in Proc. ACM SIGGRAPH,1998 ,pp. 9-20.
[36] P. Baerlocher and R. Boilic. Inverse Kinematics Techniques for the Interactive Posture Control of Articulated Figures. PhD thesis, Ecole Poly technique Federale de Lausanne, 2001.
[37] N. I. Badler, K. H. Manoochehri, and G. Walters. Articulated figure positioning by multiple constraints. IEEE Computer Graphics and Applications, 1987, Vol.7:28-38
[38] KJ Choi, etc. On-line motion retargeting. Journal of Visualization and Computer Animation, 2000, Vol.11:223-235
[39] M. Girard. Interactive design of 3D computer-animated legged animal motion. IEEE Computer Graphics and Applications, 1987,Vol.7:39-51
[40] G. T. Ke. Solving inverse kinematics constraint problems for highly articulated models. Master's thesis, University of Waterloo, 2000
[41] Richard S. Hartenberg and Jacques Denavit . Kinematic synthesis of linkages, 1964
[42] A Bruderlin, TW Calvert .Goal-directed, dynamic animation of human walking. Computer graphics and interactive techniques, 1989
[43] J Wilhelms, M Moore, R Skinner .Dynamic animation: interaction and control. The Visual Computer, 1988
[44] J Ambrósio, M Silva . Kinematic Data Consistency in the Inverse Dynamic Analysis of Biomechanical Systems. Multibody System Dynamics, 2004,219-239
[45] N Bhushan, R Shadmehr .Evidence for learning of a forward dynamic model in human adaptive control. Journal of Cognitive Neuroscience, 1998
[46] Joshua D. Webb,etc.A three dimensional forward dynamic model of a human knee for determining ligament forces. ASME International Mechanical Engineering Congress: Advances in Bioengineering, 2003
[47] HH West, ML McGuire .Dynamic programming approach to optimal feedback-feed-forward controller design. Journal of Engineering Mathematics, 1970
[48] WQD Do, DCH Yang , Inverse dynamic analysis and simulation of a platform type of robot.Journal of Robotic Systems, 1988
[49] R Happee . Inverse dynamic optimization including muscular dynamics, a new simulation method applied to goal directed movements. Journal of biomechanics, 1994
[50] Jehee Lee, etc. Interactive control of avatars animated with human motion data. Proceedings of the 29th annual conference on Computer graphics and interactive techniques, 2002,23-26
[51] B Cramblitt. Computers capture moments of motion. COMP GRAPH. WORLD,1989
[52] K Pullen, C Bregler. Motion capture assisted animation: Texturing and synthesis. International Conference on Computer Graphics and Interactive Techniques,2002
[53] VB Zordan, JK Hodgins .Motion capture-driven simulations that hit and react. ACM SIGGRAPH, 2002
[54] TB Moeslund, E Granum. A survey of computer vision-based human motion capture. Computer Vision and Image Understanding, 2001
[55] T Polichroniadis, N Dodgson. Motion blending using a classifier system.
[56] International Conference in Central Europe on Computer Graphics, Visualization and Interactive Digital Media, 1999
[57] A Witkin, Z Popovic. Motion warping. International Conference on Computer Graphics and Interactive Technique, 1999
[58] F Pighin, JP Lewis. Facial motion retargeting. ACM SIGGRAPH, 2006
[59] Seguier, Renaud, Breton, Gaspard, Stoiber, Nicolas. Facial animation retargeting and control based on a human appearance space. Computer Animation and Virtual Worlds, 2010.1, Vol.21:39-54
[60] Y Li, T Wang, HY Shum. Motion texture: a two-level statistical model for character motion synthesis. International Conference on Computer Graphics and Interactive Techniques,2002
[61] K Pullen, C Bregler .Motion capture assisted animation: Texturing and synthesis. International Conference on Computer Graphics and Interactive Techniques,2002
[62] S Moradoff, D Lischinski .Constrained synthesis of textural motion for articulated characters. The Visual Computer, 2004r
[63] M.Brand and A.Hertzmann. Style machine.ACM SIGGRAPH 2000
[64] R.Urtasun,E Glardon,R.Boulic,D.Thalmann,E Fua. Style-Based Motion Synthesis. Computer Graphics Forum,2004, vol.23:799-812
[65] Sidenbladh,H.,B1ack,M.J.,And Sigal,L.Implict probabilistic models of human motion for synthesis and tracking. European Conference on Computer Vision,2002
[66] KOVAR, L., GLEICHER, M., AND PIGHIN, F. Motion graphs. ACM SIGGRAPH 2002
[67] LEE, J, etc. Interactive control of avatars animated with human motion data. ACM SIGGRAPH 2002
[68] ARIKAN, O, etc. Interactive motion generation from examples. ACM SIGGRAPH 2002
[69] Alla Safonova, Jessica K. Hodgins .Construction and optimal search of interpolated motion graph.ACM SIGGRAPH 2007